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2acb84fe
编写于
6月 13, 2017
作者:
H
hedaoyuan
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Add ImageExpandGrad Function.
上级
61aa1098
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
237 addition
and
80 deletion
+237
-80
paddle/function/GemmConvOp.h
paddle/function/GemmConvOp.h
+1
-0
paddle/function/ImageExpandOp.cpp
paddle/function/ImageExpandOp.cpp
+181
-43
paddle/gserver/layers/BlockExpandLayer.cpp
paddle/gserver/layers/BlockExpandLayer.cpp
+52
-37
paddle/gserver/layers/BlockExpandLayer.h
paddle/gserver/layers/BlockExpandLayer.h
+3
-0
未找到文件。
paddle/function/GemmConvOp.h
浏览文件 @
2acb84fe
...
...
@@ -44,6 +44,7 @@ enum ColFormat { kCFO = 0, kOCF = 1 };
* input_channels,
* filter_height,
* filter_width]
* TODO(hedaoyuan): Refactor the arguments of the interface with TensorShape.
*/
template
<
ColFormat
Format
,
DeviceType
Device
,
class
T
>
class
Im2ColFunctor
{
...
...
paddle/function/ImageExpandOp.cpp
浏览文件 @
2acb84fe
...
...
@@ -70,16 +70,67 @@ public:
}
};
template
<
class
T
>
class
Col2ImFunctor
<
kOCF
,
DEVICE_TYPE_CPU
,
T
>
{
public:
void
operator
()(
const
T
*
colData
,
int
inputChannels
,
int
inputHeight
,
int
inputWidth
,
int
filterHeight
,
int
filterWidth
,
int
strideHeight
,
int
strideWidth
,
int
paddingHeight
,
int
paddingWidth
,
int
outputHeight
,
int
outputWidth
,
T
*
imData
)
{
for
(
int
outputH
=
0
;
outputH
<
outputHeight
;
++
outputH
)
{
for
(
int
outputW
=
0
;
outputW
<
outputWidth
;
++
outputW
)
{
for
(
int
channel
=
0
;
channel
<
inputChannels
;
++
channel
)
{
for
(
int
filterH
=
0
;
filterH
<
filterHeight
;
++
filterH
)
{
for
(
int
filterW
=
0
;
filterW
<
filterWidth
;
++
filterW
)
{
int
imRowOffset
=
outputH
*
strideHeight
+
filterH
-
paddingHeight
;
int
imColOffset
=
outputW
*
strideWidth
+
filterW
-
paddingWidth
;
int
colDataOffset
=
(((
outputH
*
outputWidth
+
outputW
)
*
inputChannels
+
channel
)
*
filterHeight
+
filterH
)
*
filterWidth
+
filterW
;
if
(
imRowOffset
>=
0
&&
imRowOffset
<
inputHeight
&&
imColOffset
>=
0
&&
imColOffset
<
inputWidth
)
{
int
imDataOffset
=
(
channel
*
inputHeight
+
imRowOffset
)
*
inputWidth
+
imColOffset
;
imData
[
imDataOffset
]
+=
colData
[
colDataOffset
];
}
}
}
}
}
}
}
};
/*
* \brief Converts the image data of four dimensions(NCHW) into
* a sequence data of three dimensions(NST). Where N is batch size,
* S is the length of the sequence after each image is expanded,
* T is the size of each time step in the sequence.
* a sequence data of three dimensions(NST) in the forward calculation,
* which is reversed in the backward calculation.
* Where N is batch size, S is the length of the sequence after each
* image is expanded, T is the size of each time step in the sequence.
*
* Arguments in forward function:
* \param inputs[0] Image data of NCHW format.
* \param outputs[0] Sequence data of NST format.
*
* Arguments in backward function:
* \param inputs[0] Sequence data of NST format.
* \param outputs[0] Image data of NCHW format.
*/
template
<
DeviceType
Device
>
class
ImageExpandFunction
:
public
FunctionBase
{
public:
void
init
(
const
FuncConfig
&
config
)
override
{
...
...
@@ -93,25 +144,27 @@ public:
numOutputs_
=
1
;
}
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
(
numInputs_
,
inputs
.
size
());
CHECK_EQ
(
numOutputs_
,
outputs
.
size
());
const
TensorShape
&
input
=
inputs
[
0
].
shape
();
const
TensorShape
&
output
=
outputs
[
0
].
shape
();
// input argument should be 4-dimensional.
CHECK_EQ
(
input
.
ndims
(),
(
size_t
)
4
);
// output argument should be 3-dimensional.
CHECK_EQ
(
output
.
ndims
(),
(
size_t
)
3
);
// The batchSize of the input needs to be equal to
// the batchSize of the output.
CHECK_EQ
(
input
[
0
],
output
[
0
]);
size_t
batchSize
=
input
[
0
];
size_t
inputChannels
=
input
[
1
];
size_t
inputHeight
=
input
[
2
];
size_t
inputWidth
=
input
[
3
];
size_t
seqLength
=
output
[
1
];
size_t
stepSize
=
output
[
2
];
virtual
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
{}
void
check
(
const
TensorShape
&
image
,
const
TensorShape
&
sequence
)
{
// image shape should be 4-dimensional.
CHECK_EQ
(
image
.
ndims
(),
(
size_t
)
4
);
// sequence shape should be 3-dimensional.
CHECK_EQ
(
sequence
.
ndims
(),
(
size_t
)
3
);
// The batchSize of the image needs to be equal to
// the batchSize of the sequence.
CHECK_EQ
(
image
[
0
],
sequence
[
0
]);
}
// Calculate the shape of colData based on the shape of the image
// and the shape of the sequence.
TensorShape
getColShape
(
const
TensorShape
&
image
,
const
TensorShape
&
sequence
)
{
size_t
inputChannels
=
image
[
1
];
size_t
inputHeight
=
image
[
2
];
size_t
inputWidth
=
image
[
3
];
size_t
seqLength
=
sequence
[
1
];
size_t
stepSize
=
sequence
[
2
];
size_t
outputHeight
=
1
+
(
inputHeight
+
2
*
paddingH
()
-
blockH
()
+
strideH
()
-
1
)
/
strideH
();
...
...
@@ -121,8 +174,59 @@ public:
CHECK_EQ
(
seqLength
,
outputHeight
*
outputWidth
);
CHECK_EQ
(
stepSize
,
inputChannels
*
blockH
()
*
blockW
());
real
*
inputData
=
inputs
[
0
].
data
<
real
>
();
real
*
outputData
=
outputs
[
0
].
data
<
real
>
();
// [output_height, output_width,
// input_channels, filter_height, filter_width]
return
TensorShape
({
outputHeight
,
outputWidth
,
inputChannels
,
(
size_t
)
blockH
(),
(
size_t
)
blockW
()});
}
protected:
std
::
vector
<
size_t
>
strides_
;
std
::
vector
<
size_t
>
paddings_
;
std
::
vector
<
size_t
>
blocks_
;
inline
int
strideH
()
const
{
return
strides_
[
0
];
}
inline
int
strideW
()
const
{
return
strides_
[
1
];
}
inline
int
paddingH
()
const
{
return
paddings_
[
0
];
}
inline
int
paddingW
()
const
{
return
paddings_
[
1
];
}
inline
int
blockH
()
const
{
return
blocks_
[
0
];
}
inline
int
blockW
()
const
{
return
blocks_
[
1
];
}
};
template
<
DeviceType
Device
>
class
ImageExpandForward
:
public
ImageExpandFunction
{
public:
void
init
(
const
FuncConfig
&
config
)
override
{
ImageExpandFunction
::
init
(
config
);
}
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
(
numInputs_
,
inputs
.
size
());
CHECK_EQ
(
numOutputs_
,
outputs
.
size
());
const
TensorShape
&
image
=
inputs
[
0
].
shape
();
const
TensorShape
&
sequence
=
outputs
[
0
].
shape
();
check
(
image
,
sequence
);
TensorShape
colShape
=
getColShape
(
image
,
sequence
);
size_t
batchSize
=
image
[
0
];
size_t
inputChannels
=
image
[
1
];
size_t
inputHeight
=
image
[
2
];
size_t
inputWidth
=
image
[
3
];
size_t
seqLength
=
sequence
[
1
];
size_t
stepSize
=
sequence
[
2
];
size_t
outputHeight
=
colShape
[
0
];
size_t
outputWidth
=
colShape
[
1
];
real
*
imageData
=
inputs
[
0
].
data
<
real
>
();
real
*
seqData
=
outputs
[
0
].
data
<
real
>
();
Im2ColFunctor
<
kOCF
,
Device
,
real
>
im2col
;
for
(
size_t
i
=
0
;
i
<
batchSize
;
i
++
)
{
// The result of im2col is [output_height, output_width,
...
...
@@ -130,7 +234,7 @@ public:
// reshape into [seqLength, stepSize], where seqLength is equal
// output_height * output_width, stepSize is equal
// input_channels * filter_height * filter_width
im2col
(
i
nput
Data
,
im2col
(
i
mage
Data
,
inputChannels
,
inputHeight
,
inputWidth
,
...
...
@@ -142,30 +246,64 @@ public:
paddingW
(),
outputHeight
,
outputWidth
,
output
Data
);
i
nput
Data
+=
inputChannels
*
inputHeight
*
inputWidth
;
output
Data
+=
seqLength
*
stepSize
;
seq
Data
);
i
mage
Data
+=
inputChannels
*
inputHeight
*
inputWidth
;
seq
Data
+=
seqLength
*
stepSize
;
}
}
};
protected:
std
::
vector
<
size_t
>
strides_
;
std
::
vector
<
size_t
>
paddings_
;
std
::
vector
<
size_t
>
blocks_
;
inline
int
strideH
()
const
{
return
strides_
[
0
];
}
inline
int
strideW
()
const
{
return
strides_
[
1
];
}
inline
int
paddingH
()
const
{
return
paddings_
[
0
];
}
template
<
DeviceType
Device
>
class
ImageExpandBackward
:
public
ImageExpandFunction
{
public:
void
init
(
const
FuncConfig
&
config
)
override
{
ImageExpandFunction
::
init
(
config
);
}
inline
int
paddingW
()
const
{
return
paddings_
[
1
];
}
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
(
numInputs_
,
inputs
.
size
());
CHECK_EQ
(
numOutputs_
,
outputs
.
size
());
// Since the implementation of Col2ImFunctor is ADD_TO,
// this function only supports ADD_TO mode.
CHECK_EQ
(
outputs
[
0
].
getArgType
(),
ADD_TO
);
const
TensorShape
&
image
=
outputs
[
0
].
shape
();
const
TensorShape
&
sequence
=
inputs
[
0
].
shape
();
check
(
image
,
sequence
);
inline
int
blockH
()
const
{
return
blocks_
[
0
];
}
TensorShape
colShape
=
getColShape
(
image
,
sequence
);
size_t
batchSize
=
image
[
0
];
size_t
inputChannels
=
image
[
1
];
size_t
inputHeight
=
image
[
2
];
size_t
inputWidth
=
image
[
3
];
size_t
seqLength
=
sequence
[
1
];
size_t
stepSize
=
sequence
[
2
];
size_t
outputHeight
=
colShape
[
0
];
size_t
outputWidth
=
colShape
[
1
];
inline
int
blockW
()
const
{
return
blocks_
[
1
];
}
real
*
imageData
=
outputs
[
0
].
data
<
real
>
();
real
*
seqData
=
inputs
[
0
].
data
<
real
>
();
Col2ImFunctor
<
kOCF
,
Device
,
real
>
col2im
;
for
(
size_t
i
=
0
;
i
<
batchSize
;
i
++
)
{
col2im
(
seqData
,
inputChannels
,
inputHeight
,
inputWidth
,
blockH
(),
blockW
(),
strideH
(),
strideW
(),
paddingH
(),
paddingW
(),
outputHeight
,
outputWidth
,
imageData
);
imageData
+=
inputChannels
*
inputHeight
*
inputWidth
;
seqData
+=
seqLength
*
stepSize
;
}
}
};
REGISTER_TYPED_FUNC
(
ImageExpand
,
CPU
,
ImageExpandFunction
);
REGISTER_TYPED_FUNC
(
ImageExpand
,
CPU
,
ImageExpandForward
);
REGISTER_TYPED_FUNC
(
ImageExpandGrad
,
CPU
,
ImageExpandBackward
);
}
// namespace paddle
paddle/gserver/layers/BlockExpandLayer.cpp
浏览文件 @
2acb84fe
...
...
@@ -47,6 +47,12 @@ bool BlockExpandLayer::init(const LayerMap& layerMap,
.
set
(
"strides"
,
strides
)
.
set
(
"paddings"
,
paddings
)
.
set
(
"blocks"
,
blocks
));
createFunction
(
backward_
,
"ImageExpandGrad"
,
FuncConfig
()
.
set
(
"strides"
,
strides
)
.
set
(
"paddings"
,
paddings
)
.
set
(
"blocks"
,
blocks
));
}
return
true
;
...
...
@@ -126,12 +132,12 @@ void BlockExpandLayer::forward(PassType passType) {
}
start
[
batchSize
]
=
batchSize
*
blockNum
;
if
(
!
useGpu_
)
{
TensorShape
input
Shape
({
batchSize
,
channels_
,
imgSizeH_
,
imgSizeW_
});
TensorShape
output
Shape
({
batchSize
,
blockNum
,
blockSize
});
inputShape_
=
Tensor
Shape
({
batchSize
,
channels_
,
imgSizeH_
,
imgSizeW_
});
outputShape_
=
Tensor
Shape
({
batchSize
,
blockNum
,
blockSize
});
BufferArgs
inputs
;
BufferArgs
outputs
;
inputs
.
addArg
(
*
getInputValue
(
0
),
inputShape
);
outputs
.
addArg
(
*
getOutputValue
(),
outputShape
,
ASSIGN_TO
);
inputs
.
addArg
(
*
getInputValue
(
0
),
inputShape
_
);
outputs
.
addArg
(
*
getOutputValue
(),
outputShape
_
,
ASSIGN_TO
);
forward_
[
0
]
->
calc
(
inputs
,
outputs
);
}
}
...
...
@@ -144,6 +150,8 @@ void BlockExpandLayer::backward(const UpdateCallback& callback) {
if
(
!
preGrad
)
{
return
;
}
if
(
useGpu_
)
{
MatrixPtr
grad
=
getOutputGrad
();
MatrixPtr
gradTrans
=
Matrix
::
create
(
blockSize
,
blockNum
,
false
,
useGpu_
);
size_t
batchSize
=
preGrad
->
getHeight
();
...
...
@@ -180,6 +188,13 @@ void BlockExpandLayer::backward(const UpdateCallback& callback) {
1.0
,
1.0
);
}
}
else
{
BufferArgs
inputs
;
BufferArgs
outputs
;
inputs
.
addArg
(
*
getOutputGrad
(),
outputShape_
);
outputs
.
addArg
(
*
getInputGrad
(
0
),
inputShape_
,
ADD_TO
);
backward_
[
0
]
->
calc
(
inputs
,
outputs
);
}
}
}
// namespace paddle
paddle/gserver/layers/BlockExpandLayer.h
浏览文件 @
2acb84fe
...
...
@@ -53,6 +53,9 @@ protected:
/// auxiliary variable, which saves the transposed output value.
MatrixPtr
outVTrans_
;
TensorShape
inputShape_
;
TensorShape
outputShape_
;
public:
explicit
BlockExpandLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
...
...
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